Protocol for quantitative nuclear magnetic resonance for deciphering electrolyte decomposition reactions in anode-free batteries

Summary In this protocol, we describe the quantification of electrolytes using nuclear magnetic resonance. We detail the steps involved for battery cycling, sample preparation, instrument operation, and data analysis. The protocol can be used to quantify electrolyte decomposition reactions and the apparent electron transfer numbers of different electrolyte components. The protocol is optimized for lithium-based anode-free batteries but can also be applied to other rechargeable batteries. For complete details on the use and execution of this protocol, please refer to Zhou et al. (2022).1


SUMMARY
In this protocol, we describe the quantification of electrolytes using nuclear magnetic resonance. We detail the steps involved for battery cycling, sample preparation, instrument operation, and data analysis. The protocol can be used to quantify electrolyte decomposition reactions and the apparent electron transfer numbers of different electrolyte components. The protocol is optimized for lithium-based anode-free batteries but can also be applied to other rechargeable batteries. For complete details on the use and execution of this protocol, please refer to Zhou et al. (2022). 1

BEFORE YOU BEGIN
In non-aqueous secondary batteries, the electrolyte decomposition reactions at the anode/electrolyte interface are considered to be the most important and complex phenomenon. 2 Electrolyte decomposition reactions lead to irreversible capacity decay and the formation of solid electrolyte interphase (SEI), which dictates the performance and stability of the anode. However, because of the complexity of simultaneous reactions of multiple components of the electrolyte and the interference of inevitably formed inactive lithium, 3 it is hard to accurately measure the change of different electrolyte components and assign the corresponding electron consumption.
In the battery community, qNMR has been proven to be a powerful tool to investigate the electrolyte composition and its evolution upon cycling in various systems. [4][5][6][7][8] Herein, the procedure of qNMR for electrolyte quantification and the recently developed electron transfer numbers (ETNs) fitting method are documented and elucidated.
Normally, the electrolyte should be prepared beforehand, though the commercialized electrolyte with known components can be used as well. The deuterated diluent that contains titrant should also be prepared. Moreover, the software for quantitative NMR (qNMR) data processing and multivariate regression analysis is also necessary, which are JEOL Delta and MATLAB in this protocol. As for the battery configuration, the anode-free battery, namely a battery without lithium metal, should be used to reflect the total irreversible capacity loss of the anode. 9 Besides, multiple samples at different C-rates are required for the subsequent apparent ETN fitting. The protocol below describes the specific steps for using coin cells with dimethyl carbonate (DMC)-based high-concentration electrolyte (HCE). We have also used this protocol in anode-free pouch cells and with other types of electrolyte. 1  Installation of the software a. The MATLAB software can be downloaded from ww2.mathworks.cn/en/products/matlab. html. After registration, a free 30-day trial can be requested. b. For permanent usage, a license can be purchased for use by commercial or government organizations, degree-granting institutions, or individuals. c. Other software with similar functionality can also be used.

KEY RESOURCES TABLE
Alternatives: This protocol has no special requirement on the source of reagents. The chemicals from other suppliers such as Sigma-Aldrich and Alfa Aesar can also be used. The use of electrode materials with different loading or type, separators with different porosity, and Cu current collectors with different types should not have an impact on the feasibility of this protocol as long as they do not change their inertness towards lithium and the extraction solution. Instruments and software with similar functions can also be used.

STEP-BY-STEP METHOD DETAILS Battery cycling
Timing: $2 weeks (depending on the charging and discharging rates) REAGENT  Considering that the electrolyte decomposition reaction is self-limited, the battery cycling step aims to repeatedly form new interfaces and accumulate electrolyte changes, which ensures the reliability and repeatability of subsequent measurements. In anode-free batteries, as there is no excessive lithium at the anode, the capacity decay mainly reflects the loss of active lithium during the lithium plating/striping process at the anode side because the anode exhibits a much lower Coulombic efficiency than the cathode. Therefore, the accumulated irreversible capacity of the lithium plating/ striping-type anode can be determined by the capacity change of the anode-free batteries. 9 It should be noted that if an anode with higher Coulombic efficiency (but still less than the cathode) is used, the capacity retention and the testing time will be prolonged.  a. Record and plot the capacity retention data using OriginPro.

Sample preparation
Timing: 2 h The sample preparation step has two main targets. One is to achieve thorough extraction of the electrolyte, which is the basis for the correct qNMR calculation. Another one is to perform the pre-titration of the remaining Li metal, which provides quantitative information on the remaining Li metal and avoids the continuous consumption of electrolytes during preparation.   CRITICAL: The d1 should be 5 times larger than the slowest longitudinal relaxation time (T1) to ensure full relaxation between the pulses. A small amount of relaxation enhancer can significantly reduce the T1 and thus shorten the testing time. 11 However, the optimal amount of relaxation enhancer may be varied in different samples. It is still suggested to measure the T1 before conducting a new experiment.  Figure 3B) [10 min].

Data analysis
Timing: 3 days a. Use the automatic peak selection and integration method provided by JEOL Delta software. An example of the raw integral data can be found in Table 1.
Optional: Special notice is that sometimes the interested peak may interfere with others, which requires further peak fitting using the Voigt function.
b. Keep the same integration width for each signal. An example of the integration results can be found in Table 2. c. At least five data points above the half-height of each peak are required to ensure integration accuracy. 21. Calculate the irreversible capacity of anode-free coin cells based on the capacity retention data and the following equations ( Figure 4B) [20 min]: a. The n th Accumulated deposition capacity= P n 1 charge capacity b. The n th Accumulated irre.capacity=1 st charge capacity À the n th discharge capacity. 22. Calculate the relative content of different electrolyte components using the equation [ Note: Here, D represents the deuterated solvent; V 1 represents the volume of diluent and V 2 is the volume of the extracted solution; m 3 is the mass of the internal standard and M IR is the molar mass of the internal standard. In this protocol, NðDMCÞ 1H is 6, NðDCBFÞ 1H is 3, NðFSIÞ 19F is 2, NðMAÞ 1H is 2, NðDCBFÞ 19F is 3, m 3 is 32.9 mg, M IR is 215.00 g mol À1 , and V 2 is 0.60 mL. The function of is to cross-calibrate the integration between different samples. An example of the calculated results can be found in Table 2, where the ''integral area'' is the raw integral data and the ''calibrated area'' represents the data after applying the cross-calibration function. Calculate the absolute loss of different electrolyte components by Dn =nðZÞ i À nðZÞ 0 .
a. Calculate the irreversible capacity caused by inactive Li0, using irre:capacity = DnðtitrantÞ i 3 F, where Dn i stands for the difference between the 0 cycle and the i cycle and F stands for the Faraday constant in mAh mol À1 . 24. Repeat steps 3-25 to gather sufficient data [2 days].
a. At least two sets of data from two different C-rates should be used.

Fit ETNs by multiple linear regression [30 min].
a. Input the consumption of solvent, the consumption of anion, and the irreversible capacity from electrolyte decomposition into the MATLAB curve fitting tool as X, Y, and Z data. b. Perform the multiple linear regression with a custom equation Z=(z 1 X+z 2 Y),F (where the faraday constant F=26.8 mAh mol À1 ) to fit the plot with the default least-squares algorithm.
Note: The choice of the fitting equation is based on the observation that both capacity decay and the consumption of electrolyte exhibits linear behavior at the test condition. This particular protocol only deals with the simple case with linear capacity decay and linear electrolyte consumption behavior. Nonlinear behavior is beyond the scope of this protocol.

EXPECTED OUTCOMES
By successfully employing the protocol, the evolution of the electrolyte and the accumulation of dead lithium can be evaluated accurately (as shown in Figure 5) with a $8 times reduced NMR testing time and a 70% reduced standard deviation, compared with the procedure without the use of relaxation enhancer, titrant, and cross-calibration. 1 This will help to quickly determine the average decomposition rates of different electrolyte components without losing accuracy. For instance, based on the results from Zhou et al., 1 the root mean square error (RMSE) of the linear fitting for solvent and anion are 0.42 mmol and 0.45 mmol, respectively, when the decomposition rates are Given that the universality of this method is not limited by the type of electrolyte, this work can also inspire the study of other battery working conditions (e.g., at high and low temperatures and calendar aging conditions), other types of anodes that do not involve excessive lithium metal (e.g., graphite and silicon-based anode) and other electrolyte components (e.g., co-solvents and functional additives).

LIMITATIONS
The main limitation of the protocol is the requirement of the anode-free, or specifically lithiummetal-free, configuration. Because of the limited titration capacity of the titrant, the batteries

OPEN ACCESS
containing a large amount of lithium metal may affect the accuracy and reproducibility of the method. The slow but somehow unavoidable reactions between titrant and other components may also exaggerate the calculation of inactive lithium metal. Besides, as an ex-situ instrument-based experiment, the quality of the results depends both on the proficient skill of sample preparation and the configuration of the NMR spectrometer, which may be compensated by repeated tests. Lastly, The qNMR measurements are limited to nuclei with high gyromagnetic ratios and natural abundances, such as 1 H and 19 F. The quantitative experiments of nuclei such as 15 N, 17 O, and 31 P are beyond the scope of this protocol and require further exploration. 11

TROUBLESHOOTING Problem 1
In the battery cycling step (step 2), the battery experience overcharging.

Potential solution
The overcharging in this protocol is mainly caused by the trace of water in battery components because DMC used as the solvent in this protocol can tolerate a high voltage of 4.3 V. Further drying, like keeping the materials in the glove box for one week, would solve this problem.

Problem 2
The batteries exhibit very inconsistent behavior in terms of the capacity decay curve (step 3).

Potential solution
Both the electrolyte and copper foil can influence the capacity decay behavior of anode-free batteries. To ensure their consistency, it is suggested to use the same batch of electrolytes and clean the copper foil with ethanol before use. Noted that, although the measured results can be normalized by the measured capacity, the consistency of cathodes is critical for evaluating the consistency of results under coordinates of the number of cycles.

Problem 3
After the sample preparation step (step 11), the extract in the NMR sample tube is turbid.

Potential solution
The black suspended solids are the fragment of the cathode that dropped during shaking. Resting the NMR tube for a while can settle the sediment at the bottom of the tube, where it would not interfere with the NMR measurement.

Problem 4
During qNMR measurement (step 17), the width of peaks broadens in cycled samples.

Potential solution
The peak broadening is caused by the high concentration of the paramagnetic substance in samples, most likely from dissolved cobalt and manganese from the cathode. Control and decreasing the amount of relaxation enhancer (m 3 ) can solve this problem.

Problem 5
The qNMR results show abnormally small quantities of MA (step 23).

Potential solution
The incomplete dissolution could cause this problem. Although the concentration of MA (0.1 M) has not reached the reported solubility of DMSO-d6 ($0.2 M), the dissolution of MA is relatively slow.
Shaking and mixing for a longer time or simply prolonging the dissolution time for 3 days can solve the problem.

Problem 6
The qNMR results show abnormal quantities of all substances (step 23).

Potential solution
Although theoretically the concentration of internal reference is known, the actual concentration of internal reference may deviate from the theoretical value due to uncontrolled factors during sample preparation steps, e.g., the internal reference may not be totally dissolved (sticks on the wall) and the volume of extract may not be the recorded value (contains insoluble sediments), or, simply due to weighing and transferring error. The cross-calibration by the in-built DMSO signal can solve this problem. For a detailed explanation, please refer to Zhou et al. 1

RESOURCE AVAILABILITY
Lead contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, Qiang Zhang (zhang-qiang@mails.tsinghua.edu.cn).

Materials availability
This study did not generate new unique reagents.
Data and code availability This paper does not report original code.
All data reported in this paper will be shared by the lead contact upon request.